基于FBG传感器和卷积神经网络的复合材料结构载荷识别研究

doi:10.11896/cldb.21040290

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Abstract Composite materials have been widely utilized in aircraft structures such as aircraft wings because of excellent performance. Therefore, recognizing the loads acting on composite materials has a significant influence on the structure design and reliability analysis of aircraft. In this work, a method based on fiber Bragg grating (FBG) sensor and convolutional neural network (CNN) was utilized to recognize the complex multi-point load. The experiment was carried out on a composite cantilever beam and the strain data was collected by sensors placed on the structure. Firstly, the strain data was input into the support vector machine (SVM) algorithm to identify the number of applied loads. Furthermore, the strain data was transformed into a rectangular picture according to the arrangement of the measuring points. After the normalization process, the picture was input into the CNN model to realize the positioning and quantification of the multi-point load. Finally, the results of CNN model were compared with the results of back-propagation neural network (BPNN) and gradient boosting decision tree (GBDT), respectively. In the experiment, the recognition accuracy of the SVM model was 99.584%. For the position of the two-point load, the mean absolute error (MAE) of the CNN model is 0.637 9 mm and 0.576 2 mm. The results indicated that the load identification method based on SVM and CNN is an effective method to identify the number of loads, the position and size of the loads applied. Additionally, the method is capable of providing a new solution for aircraft flight loads measurement.

Key words： multi-point load identification fiber Bragg grating sensor convolutional neural network support vector machine

Published: 10 January 2023 Online: 2023-01-31

CLC number:

V267

Fund:National Natural Science Foundation of China (U2141245,11772279,11972314).

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	Articles by authors
	LIU Kaiwei
	LIU Qijian
	LI Jun
	YU Yinghong
	QING Xinlin

Cite this article:

LIU Kaiwei,LIU Qijian,LI Jun, et al. Load Identification of Composite Structural Based on FBG Sensor and Convolutional Neural Network[J]. Materials Reports, 2023, 37(1): 21040290-7.

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http://www.mater-rep.com/EN/10.11896/cldb.21040290 OR http://www.mater-rep.com/EN/Y2023/V37/I1/21040290

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